class_docstr: Example agent whose learning curve is a random sigmoid.
  
  The dummy hyperparameters "width" and "height" determine the slope and
  maximum reward value reached.
code: def save_checkpoint(self, checkpoint_dir):
      path = os.path.join(checkpoint_dir, "checkpoint")
      with open(path, "w") as f:
          f.write(json.dumps({"timestep": self.timestep}))
  
file_path: ray/python/ray/tune/utils/mock_trainable.py
imports: import json
  import numpy
  import os
  from ray.tune import Trainable
label_desc: Use image node_id 3 for calling the MyTrainableClass obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint_dir) without return types